As of today, location based services have become widely available and popular among users of mobile devices. Systems, such as Global Positioning System (GPS), Global Mobile Satellite System (GMSS), Galileo, are equipped to provide location services to mobile devices whenever the mobile device is outdoors. For example, when outdoors, the use of GPS by a mobile device allows the mobile device to fairly accurately determine device location and possibly obtain or present other location related information.
However, after moving indoors, some or all of the requisite GPS signals are often unavailable or too weak to provide reliable (or any) location services. Similar problems may occur outdoors where signals from at least some of the GPS satellites are blocked by buildings or other obstacles, for example, in an urban canyon amongst high-rise buildings in a city center.
To address the lack of reliable indoor location services, alliances, such as In-Locations, consortiums, and companies have proposed proprietary systems that utilize indoor beacons based on Wi-Fi and/or Bluetooth signals. Such systems may require modification to the current Wi-Fi and Bluetooth chipsets for mobile devices as well as deployment of enabled Wi-Fi access points and Bluetooth beacons. With this architecture, the owners of the enabled beacons will be the sole service provider for that location and they will own the location information of the beacons. As a result, users have to log into to the service, or configure their devices to access (or pair with) the respective Wi-Fi or Bluetooth access points.
Another method of location when GPS location determination is not feasible uses inertial sensors, magnetometers, and barometers, which are increasingly common in mobile devices, to accurately map the movements of a mobile device as well as the user carrying the device. The mapping is based on an earlier known position of the mobile device obtained via GPS or a radio access network, such as Wi-Fi or a cellular network. Such location determinations rely on a low power, always “ON” sensor hub (e.g., a microprocessor that integrates data from different sensors on a device and manages the processing data) in the mobile device to carry out a sensor fusion algorithm and to regulate the ON time of the sensors such that the sensors do not cause the mobile device power usage to extend beyond the power envelope of the device when the device is in standby. An advantage of this type of solution, sometimes commonly known as dead reckoning, is that the device can provide reasonably accurate location and does not require costly deployment of a proprietary infrastructure (e.g., Wi-Fi/Bluetooth beacons).
The sensors required for such a dead reckoning position or movement determination are already in most modern mobile devices so that the additional cost to enable this solution is relatively low. Sensor fusion makes use of the different sensors on the mobile device such as a magnetometer, a gyroscope, an accelerometer and a barometer.
The result of the sensor fusion, however, is not a highly accurate location determination, the location is always relative to a prior position. Due to variations in the outputs from the different sensors, the accuracy of the location determinations inherently drifts over time. Also, external conditions will affect the accuracy of the sensors. For example, stray magnetic fields in the environment can cause errors in the magnetometer, or the temperature of the device may affect operation of the gyroscope or accelerometer. The use of Kalman filters can help remove some errors or otherwise reduce errors, but not all of the errors are eliminated by such filters. As a result, errors with dead reckoning solutions accumulate over time, and unless external information is provided to help remove, or mitigate, the accumulated errors, the accumulated errors increase. If not mitigated in an appropriate timely manner, the accumulated location error may increase over time to a point that the dead reckoning location information becomes unreliable.
Accordingly, there is a need for confirming the accuracy of the dead reckoning location solutions provided by the sensory navigation functions of a mobile device.